New York – July 30, 2019 – Digital technology has outsized potential to improve the running of manufacturing facilities, raising the quality of their output as well as cutting costs. Yet, many executives who invest in Big Data innovations to improve the reliability of their manufacturing facilities are increasingly aware of the challenges that come with delivering and scaling an industrial analytics transformation: while about 90 percent of respondents remain in the planning and proof-of-concept stage, only about 20 percent expect to implement solutions at scale by 2020.

New research from Bain & Company, Industrial analytics: maintenance gains without adoption pains, reveals executives that spend heavily in a bid to capture the productivity gains from industrial analytics often fail to realize a return on their investment. Therefore, identifying an approach to deliver on the promise of industrial analytics is increasingly important. The most successful asset health programs, Bain & Company finds, focus not just on the technology, but also on high-impact problems, root causes, frontline expertise and external partnership risk.

“Executives often place excessive focus on new technology, which is understandable, given the pace of innovation in analytics. Yet many leaders are already getting results with a more pragmatic, business-focused perspective,” Joachim Breidenthal, a partner in Bain & Company’s Energy & Natural Resources practice. “They recognize that asset health is about solving persistent, high-value business problems, not just the technology. And they’ve found ways to avoid doomed asset health initiatives and the battle scars they leave behind.”

Bain & Company has identified five specific ways in which asset health initiatives tend to blow up:

Deploying technology for technology’s sake

Rejecting new tools, particularly among frontline workers

Relinquishing too much control to the technology vendor

Reliance on one-size-fits-all solutions with a high-tech bias.

Falling into the trap of endless, small-scale piloting and experimentation

Leading manufacturing executives battle back against these missteps in several key ways:

Identify problems with real impact. Executives draw on a deep understanding of production pain points, downtime, maintenance costs and schedules, sector benchmarks and key business processes. They distinguish between the obvious symptoms of a problem (a series of breakdowns caused by a faulty pump, say) and the root causes (which might be inflexible work scheduling or a lack of information on the pump’s failure curve). They focus their asset health plan on these root causes, not the symptoms.

Deploy and engage key staff for mainstream adoption. Promotions, transfers and retirements can make experienced engineers scarce, eroding the communal pool of knowledge for any given asset. Successful asset health leaders find a way to include these experienced voices as well as the current maintenance teams when designing and deploying solutions. This involvement codifies institutional knowledge in the new digital tools, making the company more resilient to future personnel changes.

Choose the right vendor—and retain independence. Industrial groups often worry that new technology will not play nicely with their existing systems. They also face the challenge of vendor proliferation – one external asset health partner can rapidly become two, three or more. The worst case is being tied into a platform that siphons off valuable data into a black box—and then shuts them out of their own insights if they have the temerity to end the subscription. The more successful industrial analytics customers guard against this by starting with a view that their data is an asset to be developed, monetized and guarded. They structure vendor contracts to minimize lock-in risk, while creating roles focused on smarter industrial analytics buying. They create flexible platforms that can support multiple options for their applications.

Tailor analytics to the reality of your plant, not the purist view. Many executives can feel pressured into a purist overhaul that reinvents an outdated plant from the foundations up so that cutting-edge tools can be deployed. That can yield low returns. Over time, the attention of company leaders can also wander to easier wins. The alternative is to let asset health ambitions waste away and just maintain the unhappy status quo—or at least that’s how it can feel. The choice does not have to be this binary. Specifically, executives can use a pyramid scale to understand their company’s current asset health “maturity”, from the least mature “break-fix” state at the bottom to a state-of-the-art pinnacle of model-automated “prescriptive maintenance.” According to Bain, a typical asset health transformation that progresses from break-fix all the way up to predictive maintenance can deliver a 70 percent to 75 percent reduction in the frequency of breakdowns, while cutting downtime by 35 percent to 45 percent and maintenance costs by 25 percent to 30 percent.

The scale challenge: thinking big while still small. Leadership teams can do all of the above right, and still fall short when the solution, the supporting systems and the capabilities can’t scale up. This is why scaling remains the biggest preoccupation for managers at the industrial analytics frontline.” Companies that have bucked this trend have factored scaling into the earliest decisions on an asset health project. They ask themselves if the overall program they are creating is repeatable. But they also ask which program elements (data ingestion, say) are repeatable and could be accelerated to yield a faster solution.

“As the technology becomes more ubiquitous—even commoditized—it is vital for industrial groups to deploy it now in a way that will develop their internal capabilities in asset health and other branches of analytics,” said Edel O’Sullivan, a partner in Bain & Company’s Advanced Manufacturing Services Practice. “Scaling can be a great measure of how far they have progressed on this journey: leaders can be confident that their capabilities are maturing when their teams are moving smoothly from proof of concept to widespread roll-out. If they can see that happening, then the revolution might be back on.”